Cameras are commonly used to capture an image of a scene that includes one or more objects. Unfortunately, some of the images are blurred. For example, movement of the camera and/or movement of the objects in the scene during the exposure time of the camera can cause the image to be blurred.
A blurred captured image can be modeled as the convolution of a latent sharp image with some point spread function (“PSF”) plus noise,B=K*L+N.  Equation (1)
In Equation 1 and elsewhere in this document, B denotes a blurry image, L a latent sharp image, K a PSF kernel, and N represents noise (including quantization errors, compression artifacts, etc.). The inverse problem of recovering both the latent sharp image L and the PSF kernel K when only the blurry image B is known, is called a blind deconvolution problem.
Many blurry images include areas that further complicate the problem of determining the PSF kernel K and the latent sharp image L. For example, certain areas of a blurry image B will have a different blur PSF kernel. Thus, it is often very difficult to accurately determine the PSF kernel K and the latent sharp image L of a blurry image.